We began by calculating descriptive statistics at baseline and follow-up for each cycle. We identified nine variables that could plausibly be related to the outcomes and that differed significantly across cycles. We included these nine variables in a multiple logistic regression analysis and saved the predicted probabilities. These predicted probabilities were entered into the outcome models as propensity scores [39 (link)]. Cycle 1 was used as the reference group to compare subsequent cycles to the first cycle, as the study design allowed for modifications and refinements after each cycle through the mixed methods design [36 (link)]. We used a generalized estimating equations (GEE) approach with an exchangeable correlation matrix to logistic regression to adjust for correlations among participants within each cycle. Analyses were conducted in SAS Enterprise Guide Version 7.15 and Stata MP/IC Version 16.
Stata mp ic version 16
Manufactured by StataCorp
Stata MP/IC Version 16 is a statistical software package developed by StataCorp. It is designed for data analysis, statistical modeling, and visualization. The software provides a range of features for managing, analyzing, and presenting data. Stata MP/IC Version 16 is available in both multiprocessor (MP) and individual computer (IC) editions, catering to the needs of different users and computational requirements.
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Lab products found in correlation
3 protocols using stata mp ic version 16
1
Propensity Score Adjustment in Iterative Study
2
Educational Attainment Predictors
3
Multilevel Modeling of AOD Misuse
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Analyses were conducted in Stata MP/IC Version 16. First, descriptive statistics were run to examine frequencies and missingness. No data were missing for the variables of interest. To examine the associations between social determinants and AOD misuse, multilevel logistic regression analyses were conducted using the melogit command. Clinic, which is a proxy for community, was factored into multilevel models to account for and model the clustering at this level. Each of these models also included the categorical form of age to control for potential confounding by age. For food insecurity, we also adjusted for the number of children given birth to, which served as a proxy for children in a participant’s household; this was not assessed in the survey. This variable was recoded as a dichotomous variable (0 to 1 child as compared with 2 or more children). Associations with p-values less than 0.05 were considered statistically significant.
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